SOC 17-3025

Environmental Engineering Technologists and Technicians AI displacement risk

Field sampling, pollution monitoring, and compliance documentation support environmental engineering projects. Remote sensors automate continuous monitoring, but sample collection, decontamination, and site inspection remain fieldwork.

Exposure50

Share and intensity of work current AI systems can materially affect.

Automation28%

Likely potential for exposed tasks to move to software after workflow integration.

Risk bandModerate

Monitoring networks reduce manual reading rounds while creating demand for technicians who deploy and maintain them. Permit documentation and regulated sampling protocols keep the role compliance-anchored.

Distribution

Where Environmental Engineering Technologists and Technicians sits across 620 tracked roles

Environmental Engineering Technologists and Technicians · 32050100

Displacement pressure 32 — higher than 52% of the 620 occupations tracked on displacement.ai.

Score version

This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.

23 O*NET task statements matched to SOC 17-3025. The displayed task profile combines these official task statements with the current public score model.

Median wage context: $59,920 (May 2025, US national). The latest BLS row matched SOC 17-3025.

Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.

2030 economic stress test

How Anthropic's scenarios classify Environmental Engineering Technologists and Technicians

SOC 17-3025 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 32/100 role score and are not an occupation forecast.

Modest change

+0.4% group wage

-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.

Economy-wide: +1.6% GDP and 3.9% unemployment.

Substantial change

-0.3% group wage

-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.

Economy-wide: +8.3% GDP and 4.6% unemployment.

Extreme change

-11.5% group wage

-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.

Economy-wide: +32.4% GDP and 11.9% unemployment.

Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.

Official task evidence

O*NET task matches for Environmental Engineering Technologists and Technicians

The current evidence import matched 23 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.

Dataset31.0 (August 2026)
Matched tasks23
SOC17-3025
  • Core task / ID 19691

    Prepare and package environmental samples for shipping or testing.

  • Core task / ID 3653

    Assist in the cleanup of hazardous material spills.

  • Core task / ID 3643

    Perform environmental quality work in field or office settings.

  • Core task / ID 3640

    Receive, set up, test, or decontaminate equipment.

  • Core task / ID 3649

    Inspect facilities to monitor compliance with regulations governing substances, such as asbestos, lead, or wastewater.

  • Core task / ID 3641

    Maintain project logbook records or computer program files.

Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.

Task profile

Where AI changes the work

physical

Collect and analyze pollution samples

Exposure 36, automation 17%, augmentation 58%.

O*NET evidence: Collect and analyze pollution samples, such as air or ground water. (ID 20514)

information

Record field and laboratory data

Exposure 58, automation 33%, augmentation 66%.

O*NET evidence: Record laboratory or field data, including numerical data, test results, photographs, o... (ID 20513)

language

Prepare environmental assessment reports

Exposure 60, automation 34%, augmentation 70%.

O*NET evidence: Produce environmental assessment reports, tabulating data and preparing charts, graphs,... (ID 3654)

compliance

Inspect facilities for regulatory compliance

Exposure 34, automation 15%, augmentation 58%.

O*NET evidence: Inspect facilities to monitor compliance with regulations governing substances, such as... (ID 3649)

TaskExposureAutomationAugmentation
Collect and analyze pollution samples3617%58%
Record field and laboratory data5833%66%
Prepare environmental assessment reports6034%70%
Inspect facilities for regulatory compliance3415%58%

Transition pathways

Adjacent moves that preserve existing skills

role redesign

Environmental Compliance Specialist

Training horizon: 3-6 months. Skill overlap 68. Wage preservation signal 116.

  • Own permit documentation
  • Audit monitoring data quality
  • Manage inspection schedules
Moderate
credentialed transition

Environmental Engineer

Training horizon: 24-36 months. Skill overlap 58. Wage preservation signal 148.

  • Complete an engineering degree
  • Build remediation project experience
  • Plan for FE and PE exams
Moderate

Comparison guides

Compare the next move before you commit

What the AI risk score means for Environmental Engineering Technologists and Technicians

The displacement pressure score for Environmental Engineering Technologists and Technicians is 32. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.

For this role, the clearest risk pattern is visible at the task level. Prepare environmental assessment reports carries 34% automation pressure, while Prepare environmental assessment reports carries 70% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.

Labor-market context and wage risk

Median wage: $59,920 (May 2025, US national). Employment context: Environmental sampling and compliance support role. Typical education: Associate degree common.

Wage vulnerability is 42, while transition feasibility is 66. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.

  • Moderate displacement pressure
  • Sensor networks change data collection
  • Regulated sampling stays human

Upskilling priorities

Skills that make this role more resilient

The safest upskilling plan starts with skills already close to the work. For Environmental Engineering Technologists and Technicians, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.

Priority 1

Field sampling

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 2

Monitoring equipment

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 3

Compliance documentation

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 4

Laboratory technique

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

90-day transition plan

The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.

  1. In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
  2. By 60 days, complete one small project connected to Environmental Compliance Specialist, such as own permit documentation.
  3. By 90 days, compare internal openings and external postings for Environmental Compliance Specialist or Environmental Engineer and update your resume around measurable workflow outcomes.

FAQ

Questions about AI and Environmental Engineering Technologists and Technicians

Will AI replace Environmental Engineering Technologists and Technicians?

Field sampling, pollution monitoring, and compliance documentation support environmental engineering projects. Remote sensors automate continuous monitoring, but sample collection, decontamination, and site inspection remain fieldwork. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.

Which parts of Environmental Engineering Technologists and Technicians work are most exposed to AI?

Prepare environmental assessment reports and Record field and laboratory data show the strongest automation pressure in this model. Prepare environmental assessment reports and Record field and laboratory data are better treated as AI-augmented work.

What should Environmental Engineering Technologists and Technicians learn next?

Start with Field sampling, Monitoring equipment, Compliance documentation. The most practical adjacent paths in this model are Environmental Compliance Specialist and Environmental Engineer.

How should this score be used?

Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.

Sources

Evidence trail